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AuthorAbdeljaber, Osama
AuthorYounis, Adel
AuthorAlhajyaseen, Wael
Available date2020-09-21T08:05:52Z
Publication Date2020
Publication NameArabian Journal for Science and Engineering
CitationAbdeljaber, O., Younis, A., & Alhajyaseen, W. (2020). Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks. Arabian Journal for Science and Engineering, 1-15. https://doi.org/10.1007/s13369-020-04546-y
URIhttp://dx.doi.org/10.1007/s13369-020-04546-y
URIhttp://hdl.handle.net/10576/16211
AbstractThis paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles' paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.
Languageen
PublisherSpringerLink
SubjectTraffic safety
Signalized intersections
Turning vehicle trajectories
Convolutional neural networks
Minimum-jerk principle
TitleExtraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks
TypeArticle
Pagination1-15
dc.accessType Open Access


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